CutOut should support fill_mode="mirror"

[LukeWood]

This is actually a great "research" project in that this should strictly be better than using noise and black pixels.

[innat]

Can you add some references, sounds interesting.

[LukeWood]

There are references out there for mirroring in random translation and rotation, but not random erasing or cut out. The logic should in theory extend to cutout, but I do not have definitive proof. That's why this is also somewhat of a research effort. Happy to collaborate with anyone on benchmarks if they are interested...

[bhack]

Are you talking about (https://arxiv.org/pdf/1708.04552v2.pdf):

or something else?

Edit: Ok sorry it was in the title but not in the description. It is the "fill_mode"

[bhack]

We have some experimental results with patch gaussian as an alternative "fill_mode" for cutout:

https://arxiv.org/abs/1906.02611

[innat]

The fill_mode="mirror" sounds like a fill_mode="reflect".

[bhack]

The patch gaussian has a good citations threshod (>100).

There is a TF implementation at page 15.

I don't know if It Is better to spent time on something that has already some experimental results.

[bhack]

https://github.com/tensorflow/models/blob/master/research/object_detection/core/preprocessor.py#L2793

[innat]

@bhack Thanks for sharing this information, very useful. It's definitely an effective option.

Cutout, which improves clean accuracy but not robustness, and additive Gaussian noise, which improves robustness but hurts accuracy. To overcome this trade-off, we introduce Patch Gaussian, a simple augmentation scheme that adds noise to randomly selected patches in an input image.

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